Trans Air Pollution Qeutta final.ppt

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Transboundary Air Pollution

Badar Ghauri, Director, SUPARCOBadar Ghauri, Director, SUPARCO

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Who’s Air Do We Breathe?

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Introduction Pollution is a global problem

There are no boundaries

Satellite Remote Sensing is the only way to map global distribution of air pollution

This talk will highlight various tools available to track global/Transboundary pollution

Regional and intercontinental aerosol pollution will be discussed

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Country-to-country source-receptor matrix

Source: Foell et.al, December, 1995

(Final report submitted to The World Bank)

The columns represent the source country while the rows represent the receptor country. Shown is the total annual sulfur depositionannual sulfur deposition expressed in tones S/yr

Source/Receptor

Bangladesh Bhutan India Nepal Pakistan Sri Lanka

Bangladesh1.77E+04 1.27E+00 1.64E+04 1.77E+02 2.30E+02

4.34E-03

Bhutan3.83E+02 1.63E+02 8.14E+03 4.37E+02 8.65E+01

5.57E-18

India1.58E+04 7.14E+01 1.06E+06 5.26E+03 1.88E+04

5.49E+02

Nepal3.22E+02 1.78E+00 4.06E+04 2.21E+04 1.04E+03

1.92E-20

Pakistan0.00E+00 3.46E-09 1.73E+04 3.97E+00 1.16E+05

0.00E+00

Sri Lanka6.72E+00 4.99E-07 2.97E+03 5.70E-01 6.63E-01

8.15E+03

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Countries with Renewable Energy Targets in the Region

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Strategy on Transboundary Air Pollution

Adequacy of data compiled

Pollutants of concern (additional pollutants)

SOx, NOx, Ozone, etc (NOx shows significant increase)

Compatibility of information collected

QA/QC issues

Common monitoring protocol (such as EANET technical manual)

Adequacy of monitoring network (strengthening in terms of no. of

stations/ parameters, frequency)

National baseline studies

Integration of Monitoring data on a sub-regional basisIntegration of Monitoring data on a sub-regional basis

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Comparison of methodologies used for different pollutants

Aim for a common approach

Use of emission factors

First preference: Local emission factors for various activities

Else use emission factors such as from WB rapid emission

inventory

Undertake emission factors development (where ever deemed

necessary)

Subsequent refinements/ updating of emission inventories

Transparency in the development of inventory

Capacity Building

Analysis and refinement of National emission inventories

12


Identification of appropriate tools/ models

Model acceptability and ownership - focal centre

Integrated assessment model - effects based approach

Input data compilation - emissions/meteorology/database on critical loads

Validation: model prediction vs observed data

Capacity Building

Strengthening of regional modeling capabilities

13


Regional cooperation in cleaner energy sources (hydel, alternative energies)

Fuel quality improvement (eg., reduced S in diesel)

Improvement in energy efficiency

Sharing of information and cooperation in adoption of clean process technologies as well as EOP control technologies

Strategies to minimize air pollution

14


Damage to human health

Impacts on crop productivity, forests, etc

Related economic analysis

These would assist in formulating appropriate policy response

Studies on the impact assessment

15


Involve relevant stakeholdersIndustry, NGO’s, research institutes,

media

Dissemination of tools, methodologies, and data

Information sharing

Stakeholder involvement and information dissemination

16


Policy issues

• Financial assistance for tackling TAP: Financial assistance for tackling TAP:

Multilateral (UNEP/SACEP), National Multilateral (UNEP/SACEP), National

funds, othersfunds, others

• Scientific process to aid policy making : Scientific process to aid policy making :

Leading to signing of Agreement/ Leading to signing of Agreement/

ProtocolProtocol

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• Intergovernmental meeting, stakeholders meeting cum coordination meeting

• Participating countries should initiate the process of understanding issues arising from TAP

• Air pollution related initiatives in South Asia need to be encouraged to participate

• National Advisory Committee and National Stakeholders should coordinate activities at the national and international levels

Regional Cooperation

Male’ DeclarationMale’ Declaration

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Male declaration on control & prevention of air pollution & its likely transboundary effects

• Declaration approved on April 22, 1998Declaration approved on April 22, 1998

• Aims: intergovernmental cooperation to address TAP Aims: intergovernmental cooperation to address TAP and consequential impactsand consequential impacts

• Sets an institutional framework linking scientific Sets an institutional framework linking scientific research and policy formulation research and policy formulation

• To draw up and implement national and regional To draw up and implement national and regional action plan and protocols based on fuller action plan and protocols based on fuller understanding of TAPunderstanding of TAP

• India, Pakistan, Bangladesh, Nepal, Sri Lanka, India, Pakistan, Bangladesh, Nepal, Sri Lanka, Bhutan, Maldives and IranBhutan, Maldives and Iran

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Development • March 1998: Policy Dialogue • April 1998: Adoption of the Declaration

Implementation• Phase I: Awareness and preparation of the baseline information • Phase II: Local capacity development for monitoring and analysis

Member states

Male’ Declaration

Bhutan

Nepal

Bangladesh

Sri Lanka

Iran

Pakistan

India

Maldives

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• Strengthen the regional cooperation and stakeholders participation under the Malé Declaration;

• Strengthen the capacity building programmes initiated

• Enhance the capacity of NIAs on emission inventory development and Integrated Assessment Modeling

• Enhance the analytical and impact assessment capability at the national level through integration of findings from local pollution studies and conducting assessment studies;

• Provide decision support information for policy formulation and air pollution prevention ;

• Raise awareness for action through targeted dissemination

Objectives Objectives (April 1998)(April 1998)

Male’ DeclarationMale’ Declaration

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TerraTerra

AquaAqua

Satellite Satellite Measurement Capability

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Aerosol Optical Thickness (AOT) is the degree to which aerosols

prevent the transmission of light. Aerosol Optical Thickness also

referred to as optical depth or optical thickness depends upon

the physical constitution, the form and concentration of aerosols.

The transmissivity, which is the ratio of amount of solar radiation

incident on the surface of the earth to the amount of solar

radiation incident at the top of the atmosphere, has a value

between 0 and 1, is an indication of Aerosol optical thickness.

The smaller the transmissivity, the larger the aerosol optical

thickness. When the transmissivity is 0 the atmosphere is

perfectly opaque, and when the transmissivity is 1 the

atmosphere is perfectly transparent.

Aerosol Optical Thickness

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From AOT to Air Quality From AOT to Air Quality • Using satellite AOT to assess air quality categories

Index Values

Category Cautionary Statements PM2.5

(ug/m3) PM10

(ug/m3)

0-50 Good 0-15.4 0-54

51-100 Moderate

Unusually sensitive people should consider reducing

prolonged or heavy exertion

15.5-40.4 55-154

101-150

Unhealthy for

Sensitive

Groups

Sensitive groups should reduce prolonged or heavy

exertion 40.5-65.4 155-254

151-200 Unhealthy

Sensitive groups should avoid prolonged or heavy exertion; everyone else

should reduce prolonged or heavy exertion

65.5-150.4

255-354

201-300 Very Unhealt

hy

Sensitive groups should avoid all physical activity outdoors; everyone else

should avoid prolonged or heavy exertion

150.5-250.4

355-424

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Northern India, Pakistan, Nepal, and Bangladesh Air

Quality Event

Case Study 1:

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Map of the Air Quality Event RegionNorthern India, Pakistan, Nepal, and Bangladesh

(http://worldatlas.com/webimage/countrys/asia/lgcolor/incolor.htm)

Region of Air Quality Event

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MODIS-Aqua True Color Image Northern India, Pakistan, Nepal, and Bangladesh

February 5, 2006(http://rapidfire.sci.gsfc.nasa.gov/gallery/?2006036-0205)

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Aerosol Optical Depth (AOD) Image Northern India, Pakistan, Nepal, and Bangladesh

(Prepared by Battelle from MODIS-Aqua data using ArcView GIS processing software)

The AOD scale in this image is similar to the U.S. EPA Air Quality Index (AQI) scale, such that red-colored regions indicate unhealthy air.

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True Color and AOD Images Available on the Internet True Color Images

MODIS instrument: http://rapidfire.sci.gsfc.nasa.gov/subsets/ Click on region of interest. Click on the “display alternate dates available for this subset” link. Select date of interest (dates are in Julian date format). Repeat for all overlapping regions of interest.

True Color and AOD Images MODIS instrument: http://ladsweb.nascom.nasa.gov/browse_images/l2_browser.html

Select “Terra” or “Aqua” satellite Select appropriate “month,” “day,” and “year.” Select “parameter” from pull-down menu; “RGB” = true color images

Parasol satellite: http://www-icare.univ-lille1.fr/parasol/browse/ Click the box next to “Aerosol Optical Thickness over land” to view aerosol images.

AOD ImagesOMI instrument: http://toms.gsfc.nasa.gov/aerosols/aerosols_v8.html

Scroll to the bottom of the page to select images. Select “global image” from “Choose output” pull-down menu. Select “OMI: 8/17/2004 – Present” from “coverage satellite” pull-down menu. Select appropriate “Date to be studied.”

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Meteorological Information Available on the Internet

Hourly Weather Observations (temperature, winds, visibility, etc.)Weather Underground: http://www.wunderground.com/ Type “India” in the box at the top of the page, and hit

enter. Scroll down to the list of cities and click on “Patna”. Select the date of interest under “History and

Almanac”. Repeat for Lahore Pakistan Repeat for Kolkata (Calcutta), India. Repeat for Dhaka Bangladesh.

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Ground-Based Particulate Measurements

Indian National Air Quality Monitoring Programme (NAMP)Central Pollution Control Board: http://www.cpcb.nic.in/index.php Click on “Air” in the menu on the left side of the page. Learn about Indian air quality monitoring by clicking on the different links in the

“Air” section. Currently, no data are archived for 2006, but you can get an idea of the trends in

Indian air quality.

Nepal Ministry of Environment, Science and TechnologyAir Quality Monitoring Results: http://www.ncit.gov.np/pollution/pollution.php 24-hour average PM10 concentrations in mg m-3 for 6 sites in Nepal Click on “General Search” in menu on right-hand side of screen to search archived

data.

Pak EPA Environmental Monitoring Program (EMS)

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Long-Range Transportation of Particulate Matter (PM) and Trajectory

PM10 and PM2.5 can travel over 100 to 1000 kilometers downwind depending on the meteorological condition

This long-range transported PM always mixes with the local emissions and affects ambient air PM10 and PM2.5 levels

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A trajectory is the time integration of the position of a parcel of air as it is transported by the wind.

The parcel's passive transport by the wind is computed/reconstructed by the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model where the velocity vectors used are interpolated in both space and time. (Draxler, R.R. and Rolph, G.D., 2003)

Trajectories may be integrated both forward and backward in time

What is a trajectory?

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HYSPLIT Back Trajectory

Backward trajectories are commonly used to identify air pollution source regions and specific sources by back computation starting from the receptor

This long-range transported PM always mixes with the local emissions and affects ambient air PM10 and PM2.5 levels

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Sources of Satellite Data and Imagery Moderate Resolution Imaging Spectroradiometer (MODIS) description: http://modis-atmos.gsfc.nasa.gov/

MODIS Rapid Response System: http://rapidfire.sci.gsfc.nasa.gov/

MODIS direct broadcast site: http://eosdb.ssec.wisc.edu/modisdirect/

MODIS Level 2 LAADS data browser: http://ladsweb.nascom.nasa.gov/browse_images/l2_browser.html

The Smog Blog: http://alg.umbc.edu/usaq/

NASA Earth Observatory: http://earthobservatory.nasa.gov/

NASA Visible Earth: http://visibleearth.nasa.gov/

NASA Giovanni: http://daac.gsfc.nasa.gov/techlab/giovanni

European Space Agency (ESA) Observing the Earth: http://www.esa.int/esaEO/index.html

Tropospheric Emission Monitoring Internet Service (European): http://www.temis.nl/

Satellite Products for Europe (German Remote Sensing Data Center): http://www.dlr.de/caf/en/desktopdefault.aspx/tabid-2683/4049_read-6052/

India Meteorological Department Satellite Images and Products (INSAT satellite): http://www.imd.ernet.in/section/satmet/dynamic/insat.htm

EUMETSAT Image Gallery (Europe and Africa): http://www.eumetsat.int/Home/Main/Image_Gallery/Real-time_Images/index.htm

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True color 05-02-2006

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AOT, 05-02-2006

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MODIS Terra 28-01-2007

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Transboundary fog In winter season widespread fog and often thick fog occurs

in eastern India and northeastern Pakistan (especially the Lahore Region)

The wide spread nature of the fog can be seen using remote sensing satellite data

Fog extends approximately an area of 1500-2000 sq.km extending from eastern India to northeastern Pakistan.

During the fog visibility reduces to ~100 m.

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Transboundary fog

India ranks fourth among countries producing SO2 emissions

This part is the most industrialized part of subcontinent

During winter this region is generally in the influence of high pressure system resulting in dry seasons and low wind speeds

Analysis of the aerosols samples were performed in 1999 onwards

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METEOSAT IMAGE SHOWING FOG OVER INDIA AND NORTHERN PAKISTAN

INDIAPAKISTAN

5050

Major Thermal Power Plants in India

According to Central Electricity Authority of India, there are 83 coal fired thermal power plants

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Experimental Methods

The aerosol samples are collected on Whatman 41 filter papers using high volume samplers.

The samples are collected at Lahore from 8 A.M to 8 PM and from 8 PM to 8 A.M

The flow rate was controlled with Sieria mass flow controller at a rate of 0.7 m3/min.

An aliquot of the filter was extracted in double distilled deionized water and analyzed for SO4

2- NO3- by ion-

chromatograph using a Dionex Model 500 equipped with Peaknet software

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SO42-

NO3-

Se x 103

As x 103

Sb x 103

No. of Days

Concentrations in ug/m3 of SO42- , NO3

- , Se, As, and Sb in 12 hour samples at Lahore, Pakistan.

1 2 3 4 5 610

40

70

100

35

20

50

6

10

14

5

15

25

0

20

40

60

Sb x 103

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As x 103

NO3-

SO4-2

Se x 103

SO4-2/Se

No. of Days

Concentrations in ug/m3 of SO4 – 2, NO3

-, Se and SO4- 2/Se ratios in

aerosol samples at Lahore, Pakistan.

FogFog Clear

Con

cent

ratio

n µ

g/m

3

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The SOThe SO44-2-2 / Se ratios were suggestive of long-range / Se ratios were suggestive of long-range

transport from several hundred kms away in transport from several hundred kms away in neighbouring India. neighbouring India.

Such high concentrations pose a serious health Such high concentrations pose a serious health risk and require a more detailed study on long-term risk and require a more detailed study on long-term basis.basis.

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• The SOThe SO44

-2 -2 concentration varied from 7.29 ug/mconcentration varied from 7.29 ug/m33 to 41.89 to 41.89

ug/mug/m33 with an average 18.98 ug/m with an average 18.98 ug/m3 3 for the period.for the period.

• Se concentration varied from below the detection limit (< Se concentration varied from below the detection limit (< 1.58 ng/m1.58 ng/m33) to 5.90 ng/m) to 5.90 ng/m33, with a mean of 3.44 ng/m, with a mean of 3.44 ng/m33. .

• The high SOThe high SO44

-2-2 (72 ug/m (72 ug/m33) and Se (12.72 ng/m) and Se (12.72 ng/m33) were also ) were also

observed in samples. observed in samples.

WHAT IS BLACK CARBONWHAT IS BLACK CARBON

• It is graphitic,

• It is insoluble in water

• Chemically Inert

• Absorbs Sunlight

• Absorbs Moisture in presence of Sulfates

CITYCITY YEARYEAR BLACK CARBON BLACK CARBON

BeijingBeijing 1999-20001999-2000 8.58.5

ShanghaiShanghai 1999-20001999-2000 6.06.0

Hong KongHong Kong 1998-20021998-2002 4.24.2

TokyoTokyo 1998-19991998-1999 5.45.4

MumbaiMumbai 19991999 12.612.6

DhakaDhaka 20012001 2222

LahoreLahore 20052005 17.617.6

Mexico CityMexico City 19971997 5.85.8

New YorkNew York 20022002 <2<2

LondonLondon 19951995 2.32.3

ParisParis 1984-851984-85 3.83.8

Concentrations (ug/m3) of Black Carbon in Metropolitan Areas

0

20000

40000

60000

80000

0:00

0:55

1:50

2:45

3:55

4:50

5:45

6:55

7:50

8:50

10:0

0

10:5

5

11:5

0

12:4

5

13:4

0

14:3

5

15:4

5

16:4

0

17:3

5

19:4

0

22:2

0

0:10

2:00

2:55

4:45

5:40

6:50

7:45

8:55

BC OC

Concentrations(ug/m3)of Black Carbon in Lahore on Dec., 6-7, 2005

TimeTime

BC

(B

C ( g

/mg

/m33 ))

Health effects of airborne particles

0

20

40

60

0:03:13 2:58:13 5:53:13 8:48:13 12:55:25 15:50:25 18:45:25 21:40:25 0:35:25 3:30:25 6:25:25

Ozone

Concentrations(ug/m3) of Ozone in Concentrations(ug/m3) of Ozone in Lahore on Dec., 6-7, 2005Lahore on Dec., 6-7, 2005

TimeTime

Ozo

ne

(pp

bv)

Ozo

ne

(pp

bv)

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EPA PMF

(Positive Matrix Factorization)

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The richness of ambient air quality data sets has been

increasing in the recent years More elements being measured

Elements being stratified by particle size

Sampling duration decreases

Receptor model

An alternative for pollutant source identification contributing to

the observed chemical concentrations at a receptor site Receptor modeling utilizes composition data collected at the

receptor site to determine the source attributions. Receptor

models are based on the assumption of mass conservation and

the use of a mass balance analysis.

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PMF Positive Matrix Factorization

PMF is a receptor model for source identification and apportionment

Developed by Dr. Pentti Paatero, University of Helsinki, Finland

Application: In air quality to resolve source types (source apportionment)

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PMF Characteristics

Method: Weighted least-squares

Utilize error estimates of the data to optimum data point scaling

Does not require comprehensive advance information on source compositions

Incorporate the time variation

Obtain uncertainties for source composition and source contribution output profiles

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PMF Characteristics

Input: Ambient concentration data Uncertainty of ambient data

Observations Specified Uncertainties (for each element of each sample, having same number of rows and columns as concentration file)

Equation Based Uncertainties Output

Source compositions (F-factor)Source contributions (G-factor)Scaled residuals (eij/sij)

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Data file

Sample No. AL AS BR CE CL1 1016.86 0.56 1.29 0.59 675.832 853.37 2.61 9.63 0.71 915.453 822.65 0.99 8.23 0.31 567.134 1574.65 1.68 18.03 1.93 710.765 1074.94 0.97 11.23 0.85 693.226 2497.35 3.42 38.70 2.03 534.367 120.50 1.56 8.91 1.16 399.188 1057.57 1.02 9.72 1.02 875.769 998.97 1.43 40.43 0.93 100.0010 1719.75 1.85 51.05 1.55 1290.81

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Source Identification/Fingerprint

84

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Source Identification

Cement

AlAsBrCaCeCl Fe K LaMnNaSbScSmTi V Zn1

10

100

1000

10000

Vehicle

AlAsBrCaCeCl Fe K LaMnNaSbScSmTi V Zn

Sour

ce p

rofi

le fa

ctor

s

1

10

100

Charcoal/Wood burning


10

100

1000

Motorcycle


10

100

Sea-salt


10

100

1000

Soil


10

100

1000

10000

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Airborne Contributions of Certain Marker Species

Source Elements

Soil Al, Si, Ca, Sc, Ti, Fe, Mn, K Cement/Construction Ca, Mg Sea-salt Na, Cl, Mg Motor vehicles Br, Pb, Zn, C Refuse incineration Sb, Zn, Cd, Ag, Sn, Pb Wood burning K, C Oil combustion V, Ni, Rare earths Coal combustion As, Se, S, C, K Sulfide smelters In, Cd, As, Se, S

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• Monitoring air pollution is critical

• Ground-based information is limited

• High quality satellite measurements are now available to monitor air pollution

• Satellite information in combination with measurements and models can provide forecasts of air pollution events

SummarySummary

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Adequacy of data compiled

Pollutants of concern (additional pollutants) SOx, NOx, Ozone, etc (NOx shows significant increase)

Compatibility of information collected

QA/QC issues Common monitoring protocol (such as EANET technical

manual)

Adequacy of monitoring network (strengthening in terms of no. of stations/ parameters, frequency)

National baseline studies

Integration of Monitoring data on a sub-regional basisIntegration of Monitoring data on a sub-regional basis

92


Comparison of methodologies used for different pollutants

Aim for a common approach Use of emission factors

First preference: Local emission factors for various activities Else use emission factors such as from WB rapid emission

inventory Undertake emission factors development (where ever deemed

necessary)

Subsequent refinements/ updating of emission inventories

Transparency in the development of inventory

Capacity Building

Analysis and refinement of National emission inventories

93


Identification of appropriate tools/ models

Model acceptability and ownership - focal centre

Integrated assessment model - effects based approach

Input data compilation - emissions/meteorology/database on critical loads

Validation: model prediction vs observed data

Capacity Building

Strengthening of regional modeling capabilities

94


Regional cooperation in cleaner energy sources (hydel, alternative energies)

Fuel quality improvement (eg., reduced S in diesel)

Improvement in energy efficiency

Sharing of information and cooperation in adoption of clean process technologies as well as EOP control technologies

Strategies to minimize air pollution

95


Damage to human health

Impacts on crop productivity, forests, etc

Related economic analysis

These would assist in formulating appropriate policy response

Studies on the impact assessment

96


Involve relevant stakeholdersIndustry, NGO’s, research institutes, media

Dissemination of tools, methodologies, and data

Information sharing

Stakeholder involvement and information dissemination

97

Strategy on Transboundary Air PollutionPolicy issuesPolicy issues

• Financial assistance for tackling TAP: Financial assistance for tackling TAP:

Multilateral (UNEP/SACEP), National Multilateral (UNEP/SACEP), National

funds, othersfunds, others

• Scientific process to aid policy making : Scientific process to aid policy making :

Leading to signing of Agreement/ Leading to signing of Agreement/

ProtocolProtocol

98

Male declaration on control & prevention of air pollution & its likely transboundary effects

• Declaration approved on April 22, 1998Declaration approved on April 22, 1998

• Aims: intergovernmental cooperation to address TAP Aims: intergovernmental cooperation to address TAP and consequential impactsand consequential impacts

• Sets an institutional framework linking scientific Sets an institutional framework linking scientific research and policy formulation research and policy formulation

• To draw up and implement national and regional To draw up and implement national and regional action plan and protocols based on fuller action plan and protocols based on fuller understanding of TAPunderstanding of TAP

• India, Pakistan, Bangladesh, Nepal, Sri Lanka, Bhutan, India, Pakistan, Bangladesh, Nepal, Sri Lanka, Bhutan, Maldives and IranMaldives and Iran

99

Top GHG Emitting CountriesCO2 , CH4 , N2O, HFCs, PFCs, SF6

Country MtCO2

equivalent

% of World GHGs

1. United States2. China3. EU-254. Russia5. India6. Japan7. Germany8. Brazil9. Canada10. United Kingdom11. Italy12. South Korea13. France14. Mexico15. Indonesia

6,9284,9384,7251,9151,8841,3171,009851680654531521513512503

20.614.714.05.75.63.93.02.52.01.91.61.51.51.51.5

Contd…..Contd…..

100

Top GHG Emitting CountriesCO2 , CH4 , N2O, HFCs, PFCs, SF6

CountryMtCO2

equivalent% of World GHGs

16. Australia17. Ukraine18. Iran19. South Africa20. Spain21. Poland22. Turkey23. Saudi Arabia24. Argentina

491482480417381381355341289

1.51.41.41.21.11.11.11.00.9

25. Pakistan 285 0.8

TopRest of WorldDevelopedDeveloping

25 27,9155,75117,35516,310

83175248

Sources & Notes: WRI, CAIT. Sources & Notes: WRI, CAIT.

101

Source Contributions to Samples

g11 g12 g1p g21 . gn1 gnp

Mass Contribution

Source factor

G (n x p)

Source p

A time series plot0 10 20 30 40 50 60 70 80 90 100

0123456

Source 1

A time series plot0 10 20 30 40 50 60 70 80 90 100

0

1

2

102

Source Identification

Source p

Al As Br Ca Ce Cl Fe K La MnNa Sb ScSm Ti V Zn

1

10

100

Source 1

Al As Br Ca Ce Cl Fe K La MnNa Sb ScSm Ti V Zn

10

100

1000

f11 f12 f1m f21 fp1 fpm

Fac

tor

Element

F (p x m) Source composition

103

Aerosol ScavengingAt remote sites downwind of high emission sources SO4

2-and were found to be strongly

correlated indicating their similar atmospheric removal rates. SO42- in cloud water is

produced from both scavenging and in situ SO2 oxidation and can be expressed as

(SO2 )cw =(α/L )(SO42- )aa+ )(SO4in

2- )

where α is the fraction of aerosols SO42- taken by the cloud, L is the liquid water content

in g/m3 and (SO4in2- ) is the concentration of (SO2) oxidation.

The only source of cloud water Se is from aerosols scavenging

(Se)cw =(β/L) (Se)aa

Where β is the scavenging coefficient of aerosols Se.

Combining equ.(1) and (2)

SO4in2-=[(SO4

2-/Se)cw -(α/β) (SO42- )/ Seaa)](Se)cw

104

• The SOThe SO44-2-2/Se ratios are shown in top portion of Fig. /Se ratios are shown in top portion of Fig.

4. The ratios vary from 4. The ratios vary from 1800 to 1800 to 10000, with a 10000, with a mean of 4070, are in the range typically observed at mean of 4070, are in the range typically observed at sites in the US and in Pakistan and indicative of sites in the US and in Pakistan and indicative of substantial contributions to SOsubstantial contributions to SO44

-2-2 concentration due concentration due

to the oxidation of SOto the oxidation of SO22..

• Improved analytical techniques such as increasing Improved analytical techniques such as increasing the sampled air flow rate from 16 to 100 l/min. will the sampled air flow rate from 16 to 100 l/min. will lead improved results.lead improved results.

• The improved results helps in the detection of Se The improved results helps in the detection of Se and reduce the uncertainties in the SOand reduce the uncertainties in the SO44

-2-2/Se ratios to /Se ratios to

below 10%. below 10%.

Documents

Trans Air Pollution Qeutta final.ppt